One of the main strengths of MBRL approaches is the sample efficiency that results from using imaginary rollouts provided by the learned model. In the single-agent setting, multiple papers provide empirical evidence [1]}, [2]}, [3]} as well as theoretical justifications [4]}, [5]}, [6]} that MBRL approaches achieve near-optimal sample efficiency compared to Model Free approaches. This theory is further extended to the multi-agent setting [7]}, [8]}. However, empirical performance is still lacking due to the increased complexity of the model and exponential growth of the action and state spaces.
